# Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data

arXiv: https://arxiv.org/abs/1906.11248 · HTML: https://mlsanity.com/paper/1906.11248/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 15 of 15)

- [Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy](https://mlsanity.com/paper/2211.03796/) (2022)
- [PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models](https://mlsanity.com/paper/2403.08851/) (2024)
- [SCSS-Net: Solar Corona Structures Segmentation by Deep Learning](https://mlsanity.com/paper/2109.10834/) (2021)
- [PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework](https://mlsanity.com/paper/2106.14349/) (2021)
- [Discovering Galaxy Features via Dataset Distillation](https://mlsanity.com/paper/2311.17967/) (2023)
- [Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Supreme-Cam Wide Survey](https://mlsanity.com/paper/2501.15739/) (2025)
- [Semi-supervised Source Detection in Astronomical Images: New Benchmark and Strong Baseline](https://mlsanity.com/paper/2606.09219/) (2026)
- [Domain Adaptor Networks for Hyperspectral Image Recognition](https://mlsanity.com/paper/2108.01555/) (2021)
- [A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak Detection in Single-Frame Space Images](https://mlsanity.com/paper/2305.00412/) (2023)
- [Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation](https://mlsanity.com/paper/2309.00058/) (2023)
- [A deep learning experiment for semantic segmentation of overlapping characters in palimpsests](https://mlsanity.com/paper/2311.01130/) (2023)
- [Deep Learning Segmentation of Spiral Arms and Bars](https://mlsanity.com/paper/2312.02908/) (2023)
- [Detecting Moving Objects With Machine Learning](https://mlsanity.com/paper/2405.06148/) (2024)
- [Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning](https://mlsanity.com/paper/2409.15261/) (2024)
- [Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation](https://mlsanity.com/paper/2506.16233/) (2025)

## Abstract (arXiv)

We present Morpheus, a new model for generating pixel-level morphological classifications of astronomical sources. Morpheus leverages advances in deep learning to perform source detection, source segmentation, and morphological classification pixel-by-pixel via a semantic segmentation algorithm adopted from the field of computer vision. By utilizing morphological information about the flux of real astronomical sources during object detection, Morpheus shows resiliency to false-positive identifications of sources. We evaluate Morpheus by performing source detection, source segmentation, morphological classification on the Hubble Space Telescope data in the five CANDELS fields with a focus on the GOODS South field, and demonstrate a high completeness in recovering known GOODS South 3D-HST sources with H < 26 AB. We release the code publicly, provide online demonstrations, and present an interactive visualization of the Morpheus results in GOODS South.